1. Big Data Analytics for the Internet of Things: An Overview
Tausifa Jan Saleem and Mohammad Ahsan Chishti
2. Data, Analytics and Interoperability between Systems (IoT) is Incongruous with the Economics of Technology: Evolution of Porous Pareto Partition (P3)
Shoumen Palit Austin Datta, Tausifa Jan Saleem, Molood Barati, María Victoria López López, Marie-Laure Furgala, Diana C. Vanegas, Gérald Santucci and Eric S. McLamore
2.1. Context
2.2. Models In The Background
2.3. Problem Space: Are We Asking The Correct Questions?
2.4. Solutions Approach: Elusive Quest To Build Bridges Between Data & Decisions
2.5. Avoid This Space - The Deception Space
2.6. Solution Space - Necessary To Ask Questions That May Not Have Answers, Yet
2.7. Solution Economy - Will We Ever Get There?
2.8. Is This Faux Naïveté In Its Purest Distillate?
2.9. Reality Check - Data Fusion
2.10. 'Double A' Perspective Of Data And Tools Vs Porous Pareto (80/20) Partition
2.11. Conundrums
2.12. Stigma Of Partition Versus Astigmatism Of Vision
2.13. Illusion Of Data, Delusion Of Big Data And The Absence Of Intelligence In AI
2.14. In Service Of Society
2.15. Data Science In Service Of Society - Knowledge And Performance From Peas
2.16. Temporary Conclusion
References
Acknowledgements
3. Machine Learning Techniques for IoT Data Analytics
Nailah Afshan and Ranjeet Kumar Rout
3.1. Introduction
3.2. Taxonomy of Machine Learning Algorithms
3.2.1. Supervised ML Algorithms
3.2.1.1. Classification
3.2.1.1.1. K Nearest Neighbours (K-NN) Algorithm
3.2.1.1.2. Naïve Bayes Classifier
3.2.1.2. Regression Analysis
3.2.1.2.1. Linear Regression
3.2.1.3. Classification and Regression Tasks
3.2.1.3.1. Support Vector Machine
3.2.1.3.2. Support Vector Regression
3.2.1.3.3. Classification and regression trees
3.2.1.3.4. Random forest
3.2.1.3.5. Bootstrap Aggregating
3.2.2. Unsupervised Machine Learning Algorithms
3.2.2.1. Clustering
3.2.2.1.1. K-Means Clustering
3.2.2.1.2. Density-based spatial clustering of applications with noise (DBSCAN)
3.2.2.1.3. Neural Networks
3.2.2.2. Feature Extraction
3.2.2.2.1. Principal Component Analysis (PCA)
3.2.2.2.2. Canonical Correlation Analysis (CCA)
3.3. Conclusion
References
4. IoT Data Analytics using Cloud Computing
Anjum Sheikh, Sunil Kumar and Asha Ambhaikar
4.1. Introduction
4.2. IoT Data Analytics
4.2.1. Process of IoT Analytics
4.2.2. Types of Analytics
4.3. Cloud Computing for IoT
4.3.1. Deployment Models for Cloud
4.3.2. Service Models for Cloud Computing
4.3.3. Data Analytics on Cloud
4.4. Cloud-Based IoT Data Analytics Platform
4.4.1. Atos Codex
4.4.2. AWS IoT
4.4.3. IBM Watson IoT
4.4.4. Hitachi Vantara Pentaho, Lumada
4.4.5. Microsoft Azure IoT
4.4.6. Oracle IoT Cloud Services
4.5. Machine Learning for IoT analytics in Cloud
4.5.1. ML algorithms for Data Analytics
4.5.2. Types of Predictions supported by ML and Cloud
4.6. Challenges for Analytics using Cloud
4.7. Conclusion
References
5. Deep Learning Architectures for IoT Data Analytics
Snowber Mushtaq and Omkar Singh
5.1. Introduction
5.1.1. Types of Learning Algorithms
5.1.2. Steps involved in solving a problem
5.1.2.1. Basic Terminology
5.1.2.2. Training Process
5.1.3. Modeling in Data Science
5.1.4. Why Deep Learning and IoT?
5.2. Deep Learning Architectures
5.2.1. Restricted Boltzmann Machine
5.2.1.1. Training Boltzmann Machine
5.2.1.2. Applications of Restricted Boltzmann Machine
5.2.2. Deep Belief Networks
5.2.2.1. Training Deep Belief Networks
5.2.2.2. Applications of Deep Belief Networks
5.2.3. Auto Encoders
5.2.3.1. Training of Auto Encoders
5.2.3.2. Applications of Auto Encoders
5.2.4. Convolutional Neural Networks
5.2.4.1. Layers of Convolution Neural Network
5.2.4.2. Activation functions used in Convolution Neural Networks
5.2.4.3. Applications of Convolution Neural Networks
5.2.5. Generative Adversarial Network
5.2.5.1. Training of Generative Adversarial Network
5.2.5.2. Applications of Generative Adversarial Network
5.2.6. Recurrent Neural Networks
5.2.6.1. Training of Recurrent Neural Networks
5.2.6.2. Applications of Recurrent Neural Networks
5.2.7. Long Short Term Memory
5.2.7.1. Training of Long Short Term Memory
5.2.7.2. Applications of Long Short Term Memory
5.3. Conclusion
References
6. Adding Personal Touches to IoT: A User-Centric IoT Architecture
Sarabjeet Kaur Kochhar
6.1. Introduction
6.2. Enabling Technologies for Big Data Analytics of IoT Systems
6.3. Personalizing the IoT
6.3.1. Personalization for Business
6.3.2. Personalization for Marketing
6.3.3. Personalization for product improvement and service optimization
6.3.4. Personalization for automated recommendations
6.3.5. Personalization for improved user experience.
6.4. Related Work
6.5. User sensitized IoT architecture
6.6. Concerns and Future Directions
6.7. Conclusion
References
7. Smart Cities and the Internet of Things
Hemant Garg, Sushil Gupta and Basant Garg
7.1. Introduction
7.2. Development of Smart Cities and the Internet of Things
7.3. The combination of the internet of things with cities to form smart cities
7.3.1. Unification of the Internet of Things
7.3.2. Security of Smart Cities
7.3.3. Management of water and related amenities
7.3.4. Power Distribution and Management
7.3.5. Revenue collection and administration
7.3.6. Management of City assets and Human Resources
7.3.7. Environmental pollution management
7.4. How future smart cities can improve use of internet of things
7.5. Conclusion
References
8. A Roadmap for Application of IoT Generated Big Data in Environmental Sustainability
Ankur Kashyap
8.1. Background and motivation
8.2. Execution of study
8.2.1. Role of Big Data in sustainability
8.2.2. Present status and future possibilities of IoT in environmental sustainability
8.3. Proposed roadmap
8.4. Identification & prioritizing the barriers in the process
8.4.1. Internet infrastructure
8.4.2. High hardware & software cost
8.4.3. Less qualified workforce
8.5. Conclusion and discussion
9. Application of High-Performance Computing in Synchrophasor Data Management and Analysis for Power Grids
CM Thasnimol and R. Rajathy
9.1. Introduction
9.2. Types of Application of Synchrophasor Data
9.2.1. Voltage Stability Analysis
9.2.2. Transient Stability
9.2.3. Out of Step Splitting Protection
9.2.4. Multiple Event Detection
9.2.5. State Estimation
9.2.6. Fault Detection
9.2.7. Loss of Main (LOM) Detection
9.2.8. Topology Update Detection
9.2.9. Oscillation Detection
9.3. Utility Big Data Issues Related to PMU Driven Applications
9.3.1. Heterogeneous Measurement Integration
9.3.2. Variety and Interoperability
9.3.3. Volume and Velocity
9.3.4. Data Quality and Security
9.3.5. Utilization and Analytics
9.3.6. Visualization of Data
9.4. Big Data Analytics Platforms for PMU Data Processing
9.4.1. Hadoop
9.4.2. Apache Spark
9.4.3. Apache Hbase
9.4.4. Apache Storm
9.4.5. Cloud-Based Platforms
9.5. Conclusion
References
10. Intelligent enterprise-level big data analytics for modelling and management in smart internet of roads
Amin Fadaeddini, Babak Majidi and Mohammad Eshghi
10.1. Introduction
10.2. Fully convolutional deep neural network for autonomous vehicle identification
10.3. Experimental setup and results
10.4. Practical implementation of enterprise level big data analytics for smart city
10.5. Conclusion
References
11. Predictive analysis of intelligent sensing and cloud based integrated water management system
Tanuja Patgar and Ripal Patel
11.1. Introduction
11.2. Literature Survey
11.3. Proposed Six tier Data Framework
11.3.1. Methodology
11.3.2. Proposed Algorithn
11.4. Implementation and Result Analysis
11.4.1. Water Report for Home1 and Home2 Module
11.5. Conclusion
References
12. Data Security in the Internet-of-Things: Challenges and Opportunities
Shashwati Banerjea, Shashank Srivastava and Sachin Kumar
12.1. Introduction
12.2. Internet-of-Things (IoT): Brief introduction
12.2.1. Challenges in a Secure IoT
12.2.2. Security Requirements in IoT Architecture
12.2.2.1. Sensing layer
12.2.2.2. Network Layer
12.2.2.3. Interface Layer
12.2.3. Common Attacks in IoT
12.3. IoT Security Classification
12.3.1. Application Domain
12.3.1.1. Authentication
12.3.1.2. Authorization
12.3.1.3. Depletion of Resources
12.3.1.4. Establishment of Trust
12.3.2. Architectural Domain
12.3.2.1. Authentication in IoT architecture
12.3.2.2. Authorization in IoT architecture
12.3.3. Communication channel
12.4. Security in IoT Data
12.4.1. IoT Data Security: Requirements
12.4.1.1. Data: Confidentiality Integrity Authentication
12.4.1.2. Data Privacy
12.4.2. IoT Data Security: Research Directions
12.5. Conclusion
References
13. DDoS Attacks: Tools, Mitigation Approaches, and Probable Impact on Private Cloud Environment
Rup Kumar Deka, Dhruba Kumar Bhattacharyya and Jugal Kumar Kalita
13.1. Introduction
13.1.1. State of the Art
13.1.2. Contribution
13.1.3. Organization
13.2. Cloud and DDoS Attack
13.2.1. Cloud Deployment Models
13.2.1.1. Differences between Private Cloud and Public Cloud
13.2.2. DDoS Attacks
13.2.2.1. Attacks on Infrastructure level
13.2.2.2. Attacks on Application level
13.2.3. DoS/DDoS Attack on Cloud: Probable Impact
13.3. Mitigation Approaches
13.3.1. Discussion
13.4. Challenges and Issues with Recommendations
13.5. A Generic Framework
13.6. Conclusion and Future Work
References
14. Securing the Defense Data for Making Better Decisions using Data Fusion
Syed Rameem Zahra
14.1. Introduction
14.2. Analysis of Big Data
14.2.1. Existing IoT Big Data Analytics System
14.2.2. Big Data Analytical Methods
14.2.3. Challenges in IoT Big Data Analytics
14.3. Data Fusion
14.3.1. Opportunities provided by Data Fusion
14.3.2. Data Fusion Challenges
14.3.3. Stages at which Data Fusion can happen
14.3.4. Mathematical model for Data Fusion
14.4. Data Fusion for IoT Security
14.4.1. Defense Use Case
14.5. Conclusion
References
15. New age Journalism and Big data (Understanding big data & its influence on Journalism)
Asif Khan and Heeba Din
15.1. Introduction
15.1.1. Big Data Journalism: The Next Big Thing
15.1.2. All About Data
15.1.3. Accessing Data for Journalism
15.1.4. Data Analytics: Tool for Journalist
15.1.5. Case Studies-Big Data
15.1.5.1. BBC Big Data
15.1.5.2. The Guardian Data Blog
15.1.5.3. Wiki-leaks
15.1.5.4. World Economic Forum
15.1.6. Big Data-Indian Scenario
15.1.7. Internet of Things and Journalism
15.1.8. Impact on Media/Journalism
References
16. Two decades of big data in finance: Systematic literature review and future research agenda
Nufazil Ahangar
16.1. Introduction
16.2. Methodology
16.3. Article Identification and Selection
16.4. Description and Classification of Literature
16.4.1. Research Method Employed
16.4.2. Articles Published Year Wise
16.4.3. Journal of Publication
16.5. Content and Citation Analysis of articles
16.5.1. Citation Analysis
16.5.2. Content Analysis
16.5.2.1. Big Data in Financial Markets
16.5.2.2. Big Data in Internet Finance
16.5.2.3. Big Data in Financial Services
16.5.2.4. Big Data and other Financial Issues
16.6. Reporting of Findings and Research Gaps
16.6.1. Findings from literature review
16.6.1.1. Lack of Symmetry
16.6.1.2. Dominance of Research on financial Markets, Internet Finance and Financial Services
16.6.1.3. Dominance of Empirical Research
16.6.2. Directions for Future Research
References
